Konfiguratorius - PC builder, part prices and compatibility

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds valuable transparency by explaining that prices are refreshed at least twice a day and are 'as of its last refresh, not live at call time,' which is a critical behavioral caveat. It also states what it returns (parts, price, compatibility verdict, FPS, buy link). This goes beyond annotations and adds meaningful context without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, efficient, and front-loaded with the core purpose. It includes the price-freshness caveat and a clear list of return values. There is no wasted wording, and the structure flows logically from action to constraints to outputs. It is concise without sacrificing necessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has three parameters and no output schema, the description adequately explains what the tool returns (every part, price, compatibility verdict, FPS estimates, buy link) and its operational constraints (price freshness, supported countries). It doesn't mention any limitations like whether it returns a single build or multiple options, but that is likely inferred from 'best-value' and the singular 'PC.' Overall, an agent has enough information to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% — each parameter (budgetEur, market, compact) already has a clear description in the schema. The tool description does not add parameter-specific semantics beyond what the schema provides, but it does give context that indirectly affects parameter interpretation (e.g., 'best-value' implies budgetEur is used as a hard cap). This is sufficient given the high schema coverage, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: assembling a best-value, compatibility-checked desktop PC for a budget. It uses specific verbs ('Assemble') and a specific resource ('desktop PC'), and explicitly distinguishes itself from siblings by mentioning it includes compatibility checks and FPS estimates, which are the concerns of the other tools. An agent can easily identify what this tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (for building a PC from scratch for a budget) but does not explicitly mention the sibling tools or when to use them instead. For example, it doesn't say 'use check_compatibility if you already have parts' or 'use estimate_fps for a quick FPS check.' This is an implicit, not explicit, differentiation, so the guidance is adequate but not strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), so the bar is lower. The description adds meaningful behavioral context beyond those flags: it discloses that part names are given as free text and matched against the live catalogue, and enumerates the specific checks performed. This tells the agent how input is handled and what the tool actually evaluates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler. The first sentence front-loads the purpose and enumerates what is checked; the second covers the input format and matching behavior. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only check tool with two parameters and no output schema, the description covers purpose, input format, and behavior thoroughly. The only notable gap is that it does not describe the return format (e.g., a conflict list or pass/fail summary), which matters given no output schema exists to carry that information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real value on top: it clarifies that parts should be supplied as free-text names that get matched against a live catalogue, which informs how the agent should formulate the parts array (natural names, not catalogue IDs). The market parameter is fully covered by the schema enum and description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check') and resource ('a set of PC parts') and enumerates the exact compatibility dimensions covered (socket, chipset, RAM type, GPU clearance, cooler height, PSU headroom, connectors). This clearly distinguishes it from siblings build_pc and estimate_fps, which concern configuration and performance respectively.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the clear purpose statement – an agent facing a compatibility question would infer this tool is the right one. However, the description never explicitly mentions when not to use it or points to alternatives like build_pc or estimate_fps for adjacent needs, leaving the routing entirely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and destructiveHint=false, and the description adds behavior by specifying what the estimate includes (frame rates, 1% lows, bottleneck). This is a non-mutating estimate tool, so no side-effect disclosure is needed; the description covers the result profile beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence with no filler; the verb and core object are front-loaded, followed by the key output details and bottleneck. Every phrase carries information, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only estimator with 6 parameters and no output schema, the description names the core inputs and the output components (FPS, 1% lows, bottleneck), which is sufficient for invocation. A slightly more explicit return shape would make it fully complete, but the current text leaves no major selection or invocation gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description mentions CPU+GPU pairing, resolution, and quality preset, but it does not add meaning for game, market, setting, or resolution beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Estimate frame rates for a CPU + GPU pairing') and adds useful scope (popular games, resolution, quality preset, 1% lows, bottleneck). This clearly separates it from siblings build_pc and check_compatibility, which are about construction and compatibility rather than performance estimation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies use when an agent needs estimated gaming performance for a CPU/GPU pairing. It does not explicitly name sibling alternatives or state when not to use them, but the context is unambiguous enough for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.